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NLP – PYTHON –
ALTERYX
AGENDA
 Define Python and compare it to other programming languages
 Discuss NLP and how it is used in different industries
 Define Alteryx, explain how it is used and why it is so popular today in the marketplace
11/25/2019
Learning Objectives
2
Disclaimer : The following slides do not reflect the opinions of my employer, but
are my own personal opinions
ABOUT ME – FROM FINANCIAL PLANNING TO DATA SCIENCE
11/25/2019 4
LinkedIn post
 You are making progress even when you do not feel
you are.
 Grit
 GFC
 Reskill /Retrain
 Be uncomfortable
11/25/2019 5
Tip of the Iceberg
ABOUT ME – NEW LIFE IN DATA SCIENCE
11/25/2019 6
WHAT IS CRISP DM?
Cross Industry Standard Process for Data Mining
11/25/2019 8
▪ Data Science
▪ Data Analytics
▪ Consulting
PYTHON
 Python is an open source language
11/25/2019 10
PRE-WORK DOWNLOAD PYTHON 3.7
11/25/2019 11
MAC
11/25/2019 12
Launch Jupyter
Notebook
11/25/2019 13
MAC Users:
In Terminal, type:
jupyter notebook to
launch your notebook
Launch Jupyter
Notebook
PYTHON
 Reproducibility
 Turn your code into a slide deck for your boss
 Open Source
 Notebooks – code + output
 Data Visualisation
 Produce a report as a data analyst
 Deep Learning
 Machine Learning
11/25/2019 14
CREATE A DATABASE ENGINE IN PYTHON
11/25/2019 16
PYTHON
REFRESHER
11/25/2019 17
CONNECTING
TO A DATABASE
- PYTHON
11/25/2019 18
SELECT AND
JOIN YOUR
DATA
11/25/2019 19
CHECK WHICH
JOIN IS
APPROPRIATE
FOR YOU
11/25/2019 20
▪ Do you want to preserve user names on the left?
▪ How will you treat N/A’s?
▪ An inner join may reduce your data points
▪ What problem are you trying to answer?
https://javarevisited.blogspot.com/2012/11/how-to-join-three-tables-in-sql-query-mysql-sqlserver.html#axzz665Ch4Lx2
WRITE PYTHON
FILE TO R
11/25/2019 21
Write to CSV and read into R
Call Python from R Markdown
▪ https://rstudio.github.io/reticulate/
PYTHON WORD CLOUDS – TEXT DATA
11/25/2019
DATA UNDERSTANDING
11/25/2019 24
11/25/2019 25
DATA PREPARATION
EXPLANATORY INSIGHTS
11/25/2019 26
Imbalanced classes –
resampling methods
cross validation
PREDICTIVE MODELLING
11/25/2019 27
EVALUATE THE MODEL
11/25/2019 28
MODEL EVALUATION METRICS
11/25/2019
29
The confusion matrix, which is a breakdown of predictions into a table showing correct
predictions and the types of incorrect predictions made. Ideally, you will only see numbers
in the diagonal, which means that all your predictions were correct!
● Precision is a measure of a classifier’s exactness. The higher the precision, the more
accurate the classifier.
● Recall is a measure of a classifier’s completeness. The higher the recall, the more cases
the classifier covers.
● The F1 Score or F-score is a weighted average of precision and recall.
● Area under the ROC curve.
Source: Udacity
NLP – UNSTRUCTURED DATA
NATURAL LANGUAGE PROCESSING
11/25/2019 31
OBTAIN THE DATA
11/25/2019 32
▪ Given a new complaint comes in, We want to assign it to one of 4 categories. The classifier makes
the assumption that each new complaint is assigned to one and only one category.
WHAT IS CRISP DM?
Cross Industry Standard Process for Data Mining
11/25/2019 33
Data Understanding
Obtain Data
SPACY
11/25/2019 35
SPACY
11/25/2019 36
 Excel on steroids
 Self service analytics – you do not need to code
 What can it do?
 Workflow Optimization – compress file size, speed up
processing
 Data Preparation and Data Blending (joins)
 Writing fast and accurate expressions
 Using Macros in workflows
 Containers
 Data Parsing – Text to Columns
 Outputs files to csv, an Alteryx workflow and even a
Tableau hyper file
 Predictive analytics
11/25/2019 38
ALTERYX DESIGNER
11/25/2019 39
▪ Open source languages
Python and R do not
appear in Gartner’s 2018
magic quadrant
▪ Alteryx is a ‘challenger’ in
machine learning and data
science
Machine Learning Platforms
11/25/2019 40
Choosing the right Alteryx Tool
https://community.alteryx.com/t5/Alteryx-Community-Resources/Designer-Cheat-Sheet/ta-p/371793?attachment-id=36217
CHOOSING THE
RIGHT ALTERYX
TOOL
11/25/2019 41
WEBSCRAPING
 Python and Alteryx
 We know you want to impress your boss and do web
scraping
 Check firstly if your company allows you to scrape
someone else’s data
 Speak with your business risk team
 You may be breaching copyright laws under some
company terms and conditions
 Some websites are locked down by organizations
11/25/2019 42
11/25/2019 44
11/25/2019 45
MY TWO CENTS – DATA SCIENCE CAREER TIPS
 Meetup.com e.g data science, data engineering, Rladies, Sydney Women in Machine Learning and
Data Science, Women who code, PyData, Docker, Kubernetes, AWS
 Lunch & Learn
 Hackathons - Data Science is a team sport
 Data Science community – Get Involved
 Data Science blogs
 Learn Python, R and SQL
 Internship
11/25/2019 47
Job Boards– Buyer Beware
 Ask the recruiter what duties you will be performing
in your data scientist role
 Ask the hiring manager what your duties you will be
performing in your data scientist role
 Do not proceed to a further interview if the hiring
manager says ‘yes you will be doing reporting and
we may do data science in 18 months time’ - Run
for your life!
 Keep on applying for data science roles, get a
mentor, speak to the community, do you have
peace?
11/25/2019 48
Data Science Interviews
Roles and responsibilities
Skills & Experience
BOOKS I RECOMMEND
11/25/2019 50
HANDY
RESOURCES
 https://github.com/REMitchell/python-scraping
 Tutorials
 Blogs
 Meetups
 Data Sets – AWS, Data.gov
11/25/2019 51
TURN JUPYTER NOTEBOOK INTO SLIDES
11/25/2019 52
THANK YOU
https://www.linkedin.com/in/wendywong7/
@abc_wendsss
11/25/2019 53
Sydney WiMLDS Meetup 25 November 2019 -Technical Presentation

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Sydney WiMLDS Meetup 25 November 2019 -Technical Presentation

  • 1. NLP – PYTHON – ALTERYX
  • 2. AGENDA  Define Python and compare it to other programming languages  Discuss NLP and how it is used in different industries  Define Alteryx, explain how it is used and why it is so popular today in the marketplace 11/25/2019 Learning Objectives 2 Disclaimer : The following slides do not reflect the opinions of my employer, but are my own personal opinions
  • 3.
  • 4. ABOUT ME – FROM FINANCIAL PLANNING TO DATA SCIENCE 11/25/2019 4
  • 5. LinkedIn post  You are making progress even when you do not feel you are.  Grit  GFC  Reskill /Retrain  Be uncomfortable 11/25/2019 5 Tip of the Iceberg
  • 6. ABOUT ME – NEW LIFE IN DATA SCIENCE 11/25/2019 6
  • 7.
  • 8. WHAT IS CRISP DM? Cross Industry Standard Process for Data Mining 11/25/2019 8 ▪ Data Science ▪ Data Analytics ▪ Consulting
  • 9.
  • 10. PYTHON  Python is an open source language 11/25/2019 10
  • 11. PRE-WORK DOWNLOAD PYTHON 3.7 11/25/2019 11
  • 13. 11/25/2019 13 MAC Users: In Terminal, type: jupyter notebook to launch your notebook Launch Jupyter Notebook
  • 14. PYTHON  Reproducibility  Turn your code into a slide deck for your boss  Open Source  Notebooks – code + output  Data Visualisation  Produce a report as a data analyst  Deep Learning  Machine Learning 11/25/2019 14
  • 15.
  • 16. CREATE A DATABASE ENGINE IN PYTHON 11/25/2019 16
  • 18. CONNECTING TO A DATABASE - PYTHON 11/25/2019 18
  • 20. CHECK WHICH JOIN IS APPROPRIATE FOR YOU 11/25/2019 20 ▪ Do you want to preserve user names on the left? ▪ How will you treat N/A’s? ▪ An inner join may reduce your data points ▪ What problem are you trying to answer? https://javarevisited.blogspot.com/2012/11/how-to-join-three-tables-in-sql-query-mysql-sqlserver.html#axzz665Ch4Lx2
  • 21. WRITE PYTHON FILE TO R 11/25/2019 21 Write to CSV and read into R Call Python from R Markdown ▪ https://rstudio.github.io/reticulate/
  • 22. PYTHON WORD CLOUDS – TEXT DATA 11/25/2019
  • 23.
  • 26. EXPLANATORY INSIGHTS 11/25/2019 26 Imbalanced classes – resampling methods cross validation
  • 29. MODEL EVALUATION METRICS 11/25/2019 29 The confusion matrix, which is a breakdown of predictions into a table showing correct predictions and the types of incorrect predictions made. Ideally, you will only see numbers in the diagonal, which means that all your predictions were correct! ● Precision is a measure of a classifier’s exactness. The higher the precision, the more accurate the classifier. ● Recall is a measure of a classifier’s completeness. The higher the recall, the more cases the classifier covers. ● The F1 Score or F-score is a weighted average of precision and recall. ● Area under the ROC curve. Source: Udacity
  • 30.
  • 31. NLP – UNSTRUCTURED DATA NATURAL LANGUAGE PROCESSING 11/25/2019 31
  • 32. OBTAIN THE DATA 11/25/2019 32 ▪ Given a new complaint comes in, We want to assign it to one of 4 categories. The classifier makes the assumption that each new complaint is assigned to one and only one category.
  • 33. WHAT IS CRISP DM? Cross Industry Standard Process for Data Mining 11/25/2019 33 Data Understanding Obtain Data
  • 34.
  • 37.
  • 38.  Excel on steroids  Self service analytics – you do not need to code  What can it do?  Workflow Optimization – compress file size, speed up processing  Data Preparation and Data Blending (joins)  Writing fast and accurate expressions  Using Macros in workflows  Containers  Data Parsing – Text to Columns  Outputs files to csv, an Alteryx workflow and even a Tableau hyper file  Predictive analytics 11/25/2019 38 ALTERYX DESIGNER
  • 39. 11/25/2019 39 ▪ Open source languages Python and R do not appear in Gartner’s 2018 magic quadrant ▪ Alteryx is a ‘challenger’ in machine learning and data science Machine Learning Platforms
  • 40. 11/25/2019 40 Choosing the right Alteryx Tool https://community.alteryx.com/t5/Alteryx-Community-Resources/Designer-Cheat-Sheet/ta-p/371793?attachment-id=36217
  • 42. WEBSCRAPING  Python and Alteryx  We know you want to impress your boss and do web scraping  Check firstly if your company allows you to scrape someone else’s data  Speak with your business risk team  You may be breaching copyright laws under some company terms and conditions  Some websites are locked down by organizations 11/25/2019 42
  • 43.
  • 46.
  • 47. MY TWO CENTS – DATA SCIENCE CAREER TIPS  Meetup.com e.g data science, data engineering, Rladies, Sydney Women in Machine Learning and Data Science, Women who code, PyData, Docker, Kubernetes, AWS  Lunch & Learn  Hackathons - Data Science is a team sport  Data Science community – Get Involved  Data Science blogs  Learn Python, R and SQL  Internship 11/25/2019 47
  • 48. Job Boards– Buyer Beware  Ask the recruiter what duties you will be performing in your data scientist role  Ask the hiring manager what your duties you will be performing in your data scientist role  Do not proceed to a further interview if the hiring manager says ‘yes you will be doing reporting and we may do data science in 18 months time’ - Run for your life!  Keep on applying for data science roles, get a mentor, speak to the community, do you have peace? 11/25/2019 48 Data Science Interviews Roles and responsibilities Skills & Experience
  • 49.
  • 51. HANDY RESOURCES  https://github.com/REMitchell/python-scraping  Tutorials  Blogs  Meetups  Data Sets – AWS, Data.gov 11/25/2019 51
  • 52. TURN JUPYTER NOTEBOOK INTO SLIDES 11/25/2019 52